Spatial evolution pattern of tourism flow in China: case study of the May Day Holiday based on Baidu migration data

Abstract

ABSTRACT Tourism flows are an elementary part of mobility and an important topic in tourism research. Utilising Baidu migration data, we model tourism flows during China’s May Day holiday from 2019 to 2023. Employing hotspot analysis and the differential index for tourism flow methods, we scrutinise the spatial and temporal dynamics of tourism flows in China before, during, and after the COVID-19 pandemic. The findings are as follows: (1) Regarding temporal variations, the COVID-19 pandemic has profoundly impacted China’s tourism industry, albeit with a slow recovery currently underway. (2) Spatially, tourism flows primarily concentrated within four major city clusters, with emerging tourism destinations like Zibo gradually gaining prominence. (3) Analysing changes in tourism flow during holidays reveals that cities can be categorised into four major types and six minor types, with a predominant trend of continuous decrease in tourism flow. These findings shed light on the intricate dynamics of tourism flows in China, offering valuable insights for stakeholders in the tourism sector.